Development of an office tenant electricity use model and its application for right-sizing HVAC equipment
Bibliographic record
Abstract
As a consequence of considerable uncertainty about occupancy, occupant behaviour, and the corresponding effect on thermal loads in buildings, it is difficult to correctly size heating, ventilation, and air-conditioning (HVAC) equipment. Mechanical engineers avoid liability of potential under-capacity and corresponding thermal discomfort by making conservative assumptions about occupants. Meanwhile, there has been a surge in research on characterizing occupants through increasingly advanced modelling approaches to support building performance simulation, but these have focused on agent-based models representing individual occupants, which may be impractical for building-level HVAC equipment sizing. This paper describes the development of a data-driven stochastic tenant model using 15 months of data from 17 independent commercial tenants. The model is implemented in EnergyPlus to examine its potential for an improved HVAC equipment-sizing procedure. The results show: the standard schedules are reasonable though conservative; oversizing equipment does not greatly improve comfort; and the tremendous importance of modelling inter-tenant diversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".